DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorChen, Xen_US
dc.creatorZhang, Aen_US
dc.creatorWang, Hen_US
dc.creatorGallaher, Aen_US
dc.creatorZhu, Xen_US
dc.date.accessioned2021-08-13T06:13:28Z-
dc.date.available2021-08-13T06:13:28Z-
dc.identifier.issn1365-8816en_US
dc.identifier.urihttp://hdl.handle.net/10397/90640-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectCOVID-19en_US
dc.subjectEpidemic modelen_US
dc.subjectMobilityen_US
dc.subjectSocial distancingen_US
dc.subjectSpatial interactionen_US
dc.titleCompliance and containment in social distancing : mathematical modeling of COVID-19 across townshipsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage446en_US
dc.identifier.epage465en_US
dc.identifier.volume35en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1080/13658816.2021.1873999en_US
dcterms.abstractIn the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as the human interaction within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19, named the meso-scale Susceptible, Exposed, Infectious, Recovered (MSEIR) model, using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact epi curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results help to establish the foundation for community-level assessment and better preparedness for COVID-19.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationInternational journal of geographical information science, 2021, v. 35, no. 3, p. 446-465en_US
dcterms.isPartOfInternational journal of geographical information scienceen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85099741270-
dc.identifier.eissn1362-3087en_US
dc.description.validate202108 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera0993-n03-
dc.identifier.SubFormID2330-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextZE6Qen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2022.01.22en_US
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